Some works have proposed to use the flow-guided DConv learning to improve the performance of the video super-resolution (VSR) and video inpainting (VI) tasks.
Chan et al. [1]} proposed an offset-fidelity loss to guide the offset learning with optical flow. This loss successfully avoids the overflow of offsets and alleviates the instability problem of deformable alignment in VSR task. Subsequently, Chan et al. [2]} further proposed a flow-guided alignment layer to align the features from the reference frames to the target frames for VSR task. Li et al. [3]} designed a flow-guided feature propagation layer for video inpainting (VI) task.
